Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computerassisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recognition while linking them to an ontology of procedural phases. In this work, we adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification. Our models present comparable to state-of-the-art results for surgical tool detection and phase recognition with accuracies of 99 and 78% respectively.

Overall, this review suggests that the use of multimedia as an adjunct to conventional consent appears to improve patient comprehension. Multimedia leads to high patient satisfaction in terms of feasibility, ease of use, and availability of information. There is no conclusive evidence demonstrating a significant reduction in preoperative anxiety.

SILS appendicectomy seems to be a safe and efficacious technique. Further work in the form of randomized studies is required to investigate any significant advantages of this new and attractive technique.

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